| | from io import BytesIO |
| | import base64 |
| | import traceback |
| | import logging |
| |
|
| | from PIL import Image |
| | import torch |
| | from transformers import CLIPProcessor, CLIPModel |
| |
|
| | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| | logger = logging.getLogger(__name__) |
| | logger.setLevel('INFO') |
| |
|
| | class EndpointHandler(): |
| | def __init__(self, path=""): |
| | self.model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14").to(device) |
| | self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") |
| |
|
| | def __call__(self, data): |
| | try: |
| |
|
| | inputs = data.pop("inputs", None) |
| | text_input = None |
| | image_data = None |
| | if isinstance(inputs, Image.Image): |
| | logger.info('image sent directly') |
| | image = inputs |
| | else: |
| | text_input = inputs["text"] if "text" in inputs else None |
| | image_data = inputs['image'] if 'image' in inputs else None |
| |
|
| | if image_data is not None: |
| | logger.info('image is encoded') |
| | image = Image.open(BytesIO(base64.b64decode(image_data))) |
| |
|
| | if text_input: |
| | processor = self.processor(text=text_input, return_tensors="pt", padding=True).to(device) |
| | with torch.no_grad(): |
| | return {"embeddings": self.model.get_text_features(**processor).tolist()} |
| | elif image: |
| | |
| | processor = self.processor(images=image, return_tensors="pt").to(device) |
| | with torch.no_grad(): |
| | return {"embeddings": self.model.get_image_features(**processor).tolist()} |
| | else: |
| | return {'embeddings':None} |
| | except Exception as ex: |
| | logger.error('error doing request: %s', ex) |
| | logger.exception(ex) |
| | stack_info = traceback.format_exc() |
| | logger.error('stack trace:\n%s',stack_info) |
| | return {'Error':stack_info} |
| |
|
